murataslan1

<Window xmlns="https://github.com/avaloniaui"
        xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"
        x:Class="Calculator.MainWindow"
        Title="Cross-Platform Calculator" 
        Width="800" Height="450">
    <Button Content="Calculate" Click="CalculateButton_Click"/>
</Window>

murataslan1

<Window x:Class="Calculator.MainWindow"
        xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"
        Title="Calculator" Height="450" Width="800">
    <Grid>
        <Button Content="Calculate" Click="CalculateButton_Click"/>
    </Grid>
</Window>

murataslan1

x = 5  # Assignment
if x == 5:  # Equality check
    print("This checks equality.")

murataslan1

x = [1, 2, 3]
y = x
y.append(4)
print(x)  # Output: [1, 2, 3, 4]

murataslan1

class AIAgent:
    def __init__(self, domain_expertise, learning_rate):
        self.knowledge_base = DomainKnowledgeModel()
        self.reasoning_engine = MultiStepReasoningModule()
        self.action_executor = TaskCompletionFramework()
    
    def solve_problem(self, context):
        problem_analysis = self.reasoning_engine.analyze(context)
        action_plan = self.reasoning_engine.generate_plan(problem_analysis)
        results = self.action_executor.perform(action_plan)

murataslan1

import tensorflow as tf
from recommender_model import create_recommendation_model

# Train on massive user interaction dataset
model = create_recommendation_model()
model.fit(user_interactions_dataset)

# Intelligent, adaptive recommendations
recommendations = model.predict(new_user_profile)

murataslan1

def recommend_product(user_history):
    if 'electronics' in user_history:
        return ['laptop', 'smartphone']
    elif 'clothing' in user_history:
        return ['shoes', 't-shirt']
    # Dozens of hard-coded rules...

murataslan1

class MultiAgentSystem:  
    def solve_complex_problem(self, problem):  
        researcher = ResearchAgent()  
        architect = SystemArchitectAgent()  
        developer = CodeGenerationAgent()  
        
        research_insights = researcher.investigate(problem)  
        system_design = architect.design(research_insights)  
        solution = developer.implement(system_design)  
        

murataslan1

def decompose_complex_task(task):  
    task_steps = [  
        "research_requirement",  
        "draft_initial_solution",  
        "validate_solution",  
        "refine_and_optimize"  
    ]  
    return plan_execution(task_steps)  

murataslan1

class AIAgent:  
    def execute_task(self, task):  
        relevant_tools = self.identify_tools(task)  
        for tool in relevant_tools:  
            task_result = tool.execute(task)  
        return task_result